Image capturing devices (CMOS, CCD sensor) are usually influenced by noises during imagecapturing, which results in random noise in the videos, so it is necessary to remove the noises by means of video denoising technologies. In addition, with the development of mobile internet and as videos are becoming more and more multi-sourced, various video sources need to be displayed on display terminal devices such as a television, which include not only the conventional digital broadcast videos, but also videos from the Internet or handheld terminals. Multi-sourcing of videos poses new challenges to video denoising systems.
Video noise reduction technology includes spatial noise reduction and temporal noise reduction technologies, wherein the spatial noise reduction technology will usually result in blurring of details, while the temporal noise reduction technology can better protect details, so it is more widely used in the industry. A conventional temporal denoising system for videos is as shown in FIG. 1, wherein an inter-frame difference is calculated by means of a current frame and a previous filtered frame, then the inter-frame difference is compared with a threshold to perform motion detection. Pixels whose inter-frame difference is greater than the threshold are moving pixels, and pixels whose inter-frame difference is smaller than the threshold are still pixels. Then a filtering weight is calculated by means of the result of motion detection, and a weighted filtering is performed on the current frame and the previous filtered frame. If it is a still region, the weight for temporal weighting is larger so as to remove noise, and if it is a motion region, the weight for temporal weighting is smaller, thereby avoiding the occurrence of trailing around a moving object.
The conventional motion detection system compares a local inter-frame difference of each pixel to a threshold so as to obtain a motion probability of the pixel, so no matter how the threshold is selected, two kinds of errors are
inevitable, one is that still pixels are erroneously considered as moving pixels, and the other is that moving pixels are erroneously considered as still pixels. Since the motion detection for each pixel is independent, the motion detection results are not consistent in spatial regions, and pixels in a still region are usually erroneously detected as moving. The weight for temporal filtering is determined according to the motion probability, so in the still region, the inconsistent motion detection results will cause inconsistency in the filtering weights, accordingly, the denoising effects are not consistent in the spatial region, namely, noises of most still pixels are removed, but noises of pixels that are erroneously detected as moving are not removed. Noises of the correctly detected pixels in the adjacent area are removed, so the pixels whose noises are not removed are presented in the videos as “impulses” or “speckles”.
When the video noises satisfy an independent identically distributed Gaussian white noise distribution, pixels in the still region that have been erroneously detected as moving are usually presented as isolated points in the motion detection result, and by using this characteristic, patent U.S. Pat. No. 7,903,179B2 and patent U.S. Pat. No. 6,061,100 correct said erroneous detection results. For example, U.S. Pat. No. 7,903,179B2 provides a method for “Impulse Pattern Recognition”, which can detect the erroneously detected isolated points (Impulse Pattern) so as to remove saidisolated points. U.S. Pat. No. 6,061,100 proposes a method for “Peculiar point removal portion” to remove the isolated points.
A video processing system in a digital television chip usually performs processing in a sequence of denoising, de-interlacing and scaling, and conventional denoising systems are designed under the assumption that the video random noise is an independent identically distributed Gaussian white noise. But actually some video sources (especially video sources from the Internet) have already been subjected to some processing, such as de-interlacing, scaling, filtering, etc., before being input into the television. The noise characteristics of such de-interlaced or scaled video sources no longer have the characteristic of independence, and there is certain correlation between noises of adjacent pixels. In this case, if motion detection is performed according to the threshold comparison method, the still pixels in the still region that have been erroneously detected as moving no longer present in the form of isolated points, but they will be enlarged to connected area composed of multiple pixels, thus the isolated point detection method provided by patent U.S. Pat. No. 7,903,179B2 and patent U.S. Pat. No. 6,061,100 no longer works. On the other hand, when noises of adjacent pixels have certain correlation, the size of the interconnected region formed by pixels in the still region that have been erroneously detected as moving increases as compared to the size when the noises of adjacent pixels are independent, thus the size of “speckle” noise caused by erroneous detection will be larger than the “speckle” noise size when the noises of adjacent pixels are independent, accordingly, more serious effect is caused to the image quality.
In summary, for videos in which noises of adjacent pixels are not independent from each other, the problem of “speckle” noise occurred in the conventional temporal denoising system for videos needs to be solved.